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Substructuring for Structural Optimization in a Parallel Processing Environment
Design optimization of large structures can be attempted through a substructure strategy. In this strategy, the structure is divided into smaller substructures that are clustered to obtain a sequence of subproblems. Solution to the large problem is obtained iteratively through repeated solutions to the modest subproblems. Substructure strategies, in sequential and parallel computational environments on a Cray‐YMP computer, have been implemented in a design test bed CometBoards. The issues encountered during substructure solution and their resolution are discussed under (1) coupling and constraint formulation, (2) differences in optimal solutions, and (3) amount of computation. Coupling between subproblems and separating constraints into local and global sets promote convergence of the iterative process. The substructure strategy can converge to different local optimal designs with equal minimum weight. Substructure optimization can be computation‐intensive. However, in a parallel computational mode, it can effectively use assigned processors.
Substructuring for Structural Optimization in a Parallel Processing Environment
Design optimization of large structures can be attempted through a substructure strategy. In this strategy, the structure is divided into smaller substructures that are clustered to obtain a sequence of subproblems. Solution to the large problem is obtained iteratively through repeated solutions to the modest subproblems. Substructure strategies, in sequential and parallel computational environments on a Cray‐YMP computer, have been implemented in a design test bed CometBoards. The issues encountered during substructure solution and their resolution are discussed under (1) coupling and constraint formulation, (2) differences in optimal solutions, and (3) amount of computation. Coupling between subproblems and separating constraints into local and global sets promote convergence of the iterative process. The substructure strategy can converge to different local optimal designs with equal minimum weight. Substructure optimization can be computation‐intensive. However, in a parallel computational mode, it can effectively use assigned processors.
Substructuring for Structural Optimization in a Parallel Processing Environment
Patnaik, Surya N. (author) / Coroneos, Rula M. (author) / Hopkins, Dale A. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 15 ; 209-226
2000-05-01
18 pages
Article (Journal)
Electronic Resource
English
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